Remove Analytics Remove Data Processing Remove Structured Data Remove Unstructured Data
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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

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Reflections on the Knowledge Graph Conference 2023

Ontotext

This message resonates with the market positioning of Ontotext as a trusted, stable option for demanding data-centric use cases. During the conference, the organizers hosted a separate track called the Healthcare and Life Sciences Symposium. Its remarkable capabilities shine even brighter when delivered jointly with partners.

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New Software Development Initiatives Lead To Second Stage Of Big Data

Smart Data Collective

The emergence of massive data centers with exabytes in the form of transaction records, browsing habits, financial information, and social media activities are hiring software developers to write programs that can help facilitate the analytics process. Unstructured. Unstructured data lacks a specific format or structure.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

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How to Take Back 40-60% of Your IT Spend by Fixing Your Data

Ontotext

The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight.

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Migrate Hive data from CDH to CDP public cloud

Cloudera

Many Cloudera customers are making the transition from being completely on-prem to cloud by either backing up their data in the cloud, or running multi-functional analytics on CDP Public cloud in AWS or Azure. The Replication Manager service facilitates both disaster recovery and data migration across different environments.

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The new challenges of scale: What it takes to go from PB to EB data scale

CIO Business Intelligence

This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data. First and foremost, you need to focus on the scalability of analytics capabilities, while also considering the economics, security, and governance implications. Focus on scalability.